According to DICOM standard, which defines both medical image information and user information, a new system for digitalizing medical images is involved as a part of the main system for archiving and retrieving medical databases. The basic characteristics of this system are described in this paper. Furthermore, the analysis of some important DICOM header's tags which are used in this system, are presented, too. Having chosen the appropriate tags in order to preserve important information, the efficient system has been created.
A content-based image retrieval system where an active learning strategy is used to gain relevance feedback (RF) is described. In this way retrieving process may be highly accelerated without significant degradation of accuracy Searching procedure was performed through the two basic steps: an objective one, based on the Euclidean distances and a subjective one based on the user's relevance feedback. Images recognized from user as the best matched to a query are labeled and used for updating the query feature vector through a RBF (radial basis function) neural network. In this process user change feature vector which became more refined and appropriate for future search. In practice, several iterative steps are sufficient, as confirmed by intensive simulations
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